This specification defines the required capabilities, constraints, interfaces, performance thresholds, and verification methods for a United States Technology and Artificial Intelligence System intended to:

These requirements are informed by the strongest recurring patterns in leading digital systems: Korea's data-sharing and data-governance approach, Estonia's X-Road and â€œonce-onlyâ€ architecture, Denmark's national digital identity model, Singapore's implementation-focused National AI Strategy 2.0 and AI Verify framework, and GAO findings on the U.S. federal governmentâ€™s slow and costly modernization of legacy IT.

The United States shall operate a national technology and AI ecosystem in which:

3.2 Problem Statement Current U.S. performance is weakened by fragmentation, aging systems, inconsistent interoperability, weak public trust, and heavy spending on maintenance instead of transformation. GAO reported that the federal government spends over $100 billion annually on IT and cyber-related investments, with about 80% going to operations and maintenance of existing systems, including legacy systems that increase cost and cybersecurity risk.

TAI-MIS-001 - Universal Digital Participation The system shall enable every household, business, school, library, and public service organization in the United States to participate in the digital economy and digital government.

The system shall be designed to improve measurable public outcomes, including affordability, access, service speed, service quality, productivity, and public trust.

The system shall preserve privacy, due process, civil rights, freedom of expression, and meaningful human recourse.

The system shall provide a national broadband baseline adequate for work, education, telehealth, civic participation, and AI-enabled services.

The system shall include permanent affordability mechanisms for low-income households and underserved populations.

The system shall promote market and policy conditions under which most households have more than one practical high-speed provider option.

The system shall support migration to gigabit-capable fixed infrastructure and high-performance mobile access.

The system shall provide backup power, redundancy, cyber resilience, and continuity capability for critical communications infrastructure.

Denmark's public-service model shows the value of a national eID used as the primary key to digital public services, while preserving structured access to public self-service.

The system shall provide a secure, privacy-protective digital identity framework for access to government digital services.

The system shall support multiple secure authentication methods, including device-based and recovery-based methods.

The identity framework shall disclose only the minimum data required for each authorized transaction.

The identity framework shall support people with disabilities, limited English proficiency, low digital literacy, and limited device access.

The system shall preserve secure non-digital access channels for essential services.

Korea's OECD review emphasizes secure data-sharing and reuse under legal and institutional frameworks, while Estonia's X-Road model demonstrates interoperable distributed systems, the once-only principle, and protections for privacy and data integrity.

The system shall provide a secure interoperability layer enabling authorized data exchange across agencies and levels of government.

The system shall prevent agencies from requiring an individual or business to submit information repeatedly when that information is already lawfully available through authorized data exchange.

The system shall support interoperable distributed data management rather than requiring a single centralized super-database.

The system shall log data access, use, modification, and transfer events in a tamper-evident manner.

The system shall allow individuals to view which authorized entities accessed their personal data and for what approved purpose.

The system shall provide procedures for identifying, contesting, correcting, and propagating corrections to erroneous personal data.

The system shall support end-to-end digital completion of major public services, including application, verification, status tracking, decision, appeal, and record access.

The system shall provide integrated access to major public services through consolidated user-facing portals.

The system shall organize priority services around life events such as birth, education, employment, business formation, illness, disability, retirement, and death.

The system shall use plain language, accessible design, and validated user-centered workflows.

Where legally authorized, the system shall support proactive service initiation using verified existing data.

The system shall reduce completion time, duplicate data entry, paperwork burden, and avoidable human processing steps.

Singapore's official AI strategy emphasizes widespread adoption with implementation discipline, while AI Verify provides a structured testing and assurance approach.

All government AI systems and all AI systems used in high-impact regulated contexts shall be classified by risk level.

The system shall maintain an inventory of AI systems in development, testing, pilot use, and production use.

High-impact AI systems shall undergo documented testing for accuracy, robustness, privacy, security, bias, and foreseeable misuse before operational deployment.

Generative, agentic, or decision-support AI systems used in sensitive environments shall undergo adversarial and red-team evaluation.

Any AI-assisted decision materially affecting rights, benefits, liberty, education, healthcare, employment, or legal status shall provide meaningful human review and appeal.

High-impact AI systems shall have documented intended use, limits, data provenance, evaluation results, monitoring procedures, and rollback triggers.

Operational AI systems shall be monitored for drift, degradation, abuse, security compromise, and harmful outputs.

Material AI failures, harmful incidents, privacy breaches, and major security events shall be reported, investigated, and remediated.

Government procurement of AI systems shall require auditability, security evidence, documentation, and assurance artifacts.

GAO continues to find that U.S. legacy modernization delays create cost, schedule, cybersecurity, and privacy risks.

All critical digital and AI systems shall use secure-by-design and secure-by-default principles.

The system shall enforce least-privilege access, network segmentation, and role-based data authorization.

The system shall encrypt sensitive data in transit and at rest using approved methods.

Critical services shall maintain tested continuity, backup, disaster recovery, and restoration procedures.

Agencies shall establish and execute funded schedules for retiring or replacing obsolete legacy systems.

The system shall support universal digital literacy instruction sufficient for safe and productive participation in modern civic and economic life.

The system shall provide age-appropriate AI literacy across K-12, higher education, workforce training, and adult education.

The system shall provide educators with training and support for effective and responsible use of technology and AI.

The system shall provide reskilling and upskilling pathways for workers affected by digital and AI transformation.

Government agencies shall maintain sufficient internal technical expertise to procure, evaluate, govern, and oversee digital and AI systems competently.

The system shall prefer open standards, documented interfaces, and portable data formats.

Individuals and organizations shall be able to export and transfer their core digital records and service relationships without unreasonable barriers.

Public technology investments shall avoid architectural dependency on single vendors when functionally equivalent open or portable alternatives are feasible.

The system shall provide clear service, pricing, and performance disclosures for consumer-facing digital infrastructure and services where public policy or public subsidy is involved.

The interfaces shall comply with accessibility requirements, plain-language design, and multilingual support.

5.2 System Interfaces The system shall provide secure machine-to-machine interfaces using documented APIs, identity controls, audit logging, and common data standards.

5.3 Oversight Interfaces The system shall provide dashboards, logs, and reporting interfaces sufficient for inspection, audit, certification, incident review, and public accountability.

The system shall operate within constitutional protections, statutory privacy limits, due process requirements, and civil-rights obligations.

The system shall be modular, interoperable, auditable, and capable of incremental deployment and replacement.

The system shall not make essential services effectively unavailable to people who cannot or do not use advanced digital tools.

The system shall not require blind trust in opaque AI or administrative automation where rights or material interests are at stake.

No requirement shall be considered satisfied until the designated verification evidence is complete and recorded.

Each requirement in this specification should trace to one or more of the following:

TAI-DAT-002 Once-Only Principle traces to public burden reduction, interoperability, and best-practice benchmarks from Estonia and Korea.

TAI-ID-001 Secure Digital Identity traces to secure public-service access and Denmark's digital-service model.

TAI-AI-003 Pre-Deployment Testing traces to trustworthy deployment and Singapore's AI assurance emphasis.

TAI-SEC-006 Legacy Retirement Schedule traces to GAO-documented modernization delays and risk reduction.

This specification requires the United States to build a technology and AI system that is:

It is designed to correct the specific U.S. pattern of spending heavily while remaining burdened by fragmented services and aging systems, and to move the country closer to the practical strengths demonstrated by Korea, Estonia, Denmark, and Singapore.

Government Spending Metrics

Executive Summary — What the Numbers Mean

This dashboard separates Government Spending into three questions: whether fiscal data is complete enough to support a trustworthy public grade, whether related bills are moving through Congress, and whether responsibility can be attributed to the right people. The current fiscal confidence score is 75.5, grade C. The current legislative progress score is 34.3, grade F. The role-aware attribution layer is Internal Review Ready. A low legislative progress grade means the issue is not moving through Congress; it is not the same thing as a cost estimate.

Last Full Metrics Refresh: 2026-05-05 17:11:05 Role coverage: Coverage file not found Attribution status: Current

Layer	Question Answered	How to Use It
Cost / Fiscal Confidence	Do we have enough fiscal classification, amount, and official-estimate data to support a trustworthy grade?	Use this to judge data quality before treating a score as public-facing.
Legislative Progress	Are Government Spending bills moving through Congress, stalling, or becoming stale?	Use this as an accountability warning about congressional action or inaction.
Role-Aware Attribution	Can responsibility be separated among sponsors, cosponsors, committee leaders, and chamber leaders?	Use this to avoid blaming the wrong person when a bill stalls.

Public-readiness caution: These metrics are designed to support a transparent public demonstration, but the dashboard should not overstate certainty. Cost confidence, legislative progress, and accountability attribution should be read together.

Cost-Estimate Interpretation: The Government Spending cost-estimate metrics are ready for public demonstration with normal source and confidence disclosures.
Legislative Progress Interpretation: The legislative progress metrics are not ready for public grading. The current data show that most Government Spending bills have not moved beyond introduction or referral and have stale latest-action dates.
Accountability Attribution Interpretation: The role-aware accountability attribution layer is ready for internal review. It identifies sponsor/cosponsor credit separately from committee and floor leadership review, but does not assign individual leadership penalties yet.
Cost Snapshot: 2026-05-04T21:25:35Z. Loaded: C:\WEB\VoiceToCongress.com\40 - Top Issues\050 - Government Spending\40 - Metrics\App_Data\metrics_current.json
Progress Snapshot: 2026-05-04T21:25:35Z. As of: 2026-05-04. Loaded: C:\WEB\VoiceToCongress.com\40 - Top Issues\050 - Government Spending\40 - Metrics\App_Data\legislative_progress_current.json
Attribution Snapshot: 2026-05-04T21:25:35Z. Loaded: C:\WEB\VoiceToCongress.com\40 - Top Issues\050 - Government Spending\40 - Metrics\App_Data\government_spending_accountability_attribution_current.json

System Score Summary

Cost Confidence Score	75.5	Cost Grade	C	Cost Readiness	Public Demonstration Ready
Legislative Progress Score	34.3	Progress Grade	F	Progress Readiness	Not Ready
Attribution Readiness	Internal Review Ready	Sponsor Credit Records	268	Leadership Review Rate	100.0%
Bill Count	268	Fiscal Assessment Coverage	100.0%	Needs Review Rate	9.7%

Cost-Estimate Data Confidence

Metric	Current Value	Count	Meaning
Fiscal Assessment Coverage	100.0%	268 / 268	Percent of Government Spending bills with a fiscal assessment row.
Resolved Fiscal Classification Coverage	90.3%	242 / 268	Percent of bills where the fiscal classification is resolved and no longer in the review backlog.
Confirmed Official Estimate Coverage	0.0%	0 / 268	Percent of bills with official CBO/JCT/official cost-estimate metadata. Auto-classified bills are not counted here.
Usable Amount Data Coverage	83.6%	224 / 268	Percent of bills with usable amount data, fixed bill-text amounts, ranges, or amount classifications suitable for fiscal metrics.
Fiscal Impact Determination Coverage	85.4%	229 / 268	Percent of bills where the system has determined whether and how the bill affects spending, revenue, assets, administration, or fiscal exposure.
Needs Review Rate	9.7%	26 / 268	Percent of bills still requiring human review or additional automation.

Fiscal Classification Summary

Classification Area	Count	Use
Fiscal Significant Bills	225	Bills identified as having spending, revenue, asset, administrative, procurement, personnel, transfer, grant, open-ended, or other fiscal relevance.
Open-Ended Fiscal Exposure	46	Bills with open-ended or contingent fiscal authority, including language such as "such sums as are necessary". These are not treated as zero cost.
Fixed Bill-Text Amounts	28	Bills where a fixed dollar amount was detected in bill text near fiscal authorization or appropriation language.
Official Estimate Metadata	0	Bills where official CBO/JCT/official cost-estimate metadata was found. Dollar amount parsing from CBO documents is a separate future step.

Legislative Progress and Action Staleness

Metric	Current Value	Count	Meaning
Introduced or Referred	97.8%	262 / 268	Bills that have not moved beyond introduction or committee referral. This is normal early in a process, but weak evidence of legislative progress.
Advanced Bills	2.2%	1 committee activity, 5 reported, 0 passed one chamber	Bills with committee activity, committee reporting, chamber passage, or enactment. This is stronger evidence that Congress acted on the issue.
Stale or Dormant	100.0%	268 / 268	Bills with no recent meaningful action. This is a legislative accountability warning, not a cost estimate.
Became Law	0	0 / 268	Bills that completed the legislative process and became law.
Average Stage Score	19.8	0 to 100	Average progress stage score across the Government Spending bill universe.
Average Action Staleness Score	16.4	0 to 100	Average recency score. Lower values mean the bill universe is stale or dormant.

CBO Estimate Eligibility

CBO Status	Count	Rate	Meaning
Not Yet Expected	259	96.6%	No official CBO estimate has been found, but the bill appears to be introduced/referred only, so an estimate may not yet be expected.
Watch - Committee Activity	1	Information	The bill has some committee activity but no official CBO estimate metadata found yet.
Expected but Missing	5	1.9%	The bill appears advanced enough that a missing estimate is more concerning and should be reviewed.
Official Estimate Found	3	1.1%	Official CBO estimate metadata has been found.

Role-Aware Accountability Attribution

Attribution Area	Count	Use
Sponsor Initiative Credit	268	Sponsors receive credit for introducing or carrying a Government Spending bill. Lack of later movement should not automatically reduce the sponsor score.
Cosponsor Support Credit	189	Cosponsors receive support credit. The system should not automatically penalize cosponsors when committee or chamber leadership does not move the bill.
Committee Leadership Review Needed	263	Bills appear stalled before meaningful committee advancement. Committee jurisdiction, chair, ranking member, and referral data are needed before assigning individual responsibility.
Floor Leadership Review Needed	5	Bills appear reported or calendar-ready but have not received a chamber vote. Chamber leadership and floor scheduling data are needed before assigning individual responsibility.
CBO Gap Review Needed	5	Bills appear advanced enough that missing official CBO estimate metadata should be reviewed.
Leadership Review Rate	100.0%	Percent of bills requiring committee or floor leadership review before a fair member-level report card can assign responsibility.

What These Metrics Mean

Confirmed official estimate coverage is intentionally strict. It counts official cost-estimate metadata only. It does not count open-ended appropriations, fixed bill-text amounts, or automated fiscal classifications as CBO estimates.
Resolved fiscal classification coverage shows how much of the Government Spending bill universe has been classified well enough for internal metrics review.
Legislative progress is separate from fiscal impact. A bill can be fiscally significant but still have little or no legislative progress.
Stale or dormant status measures inaction. This is potentially important for future congressional report cards.
CBO estimate eligibility prevents the system from unfairly penalizing introduced-only or referred-only bills that may not normally have a CBO estimate yet.
Role-aware accountability attribution separates sponsor/cosponsor initiative credit from committee and chamber leadership review. It prevents the report card from punishing the wrong person when a bill stalls.

Suggested Public Disclosure

Government Spending fiscal classifications are internally review-ready. The metrics distinguish official cost estimates from automated fiscal classifications, and bills with open-ended fiscal exposure are not treated as zero cost. Legislative progress metrics show whether bills are advancing, stalled, stale, or awaiting review; this score should be treated as an accountability warning, not as a cost estimate. Role-aware attribution is now available for the current target set, so sponsor/cosponsor initiative can be separated from committee or chamber leadership review. Public grades should still disclose data confidence, official-estimate coverage, amount coverage, and attribution readiness.

Requirements - Technology and AI

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